CDC Methods: Laying the Groundwork for Effective Change Data Capture

In the ever-evolving terrain of data management, replicating incremental database changes is critical to preserving data integrity and ensuring real-time availability. But that’s not enough. For Change Data Capture (CDC) to be effective it needs to be implemented differently in different types of databases. Not all CDC engines are born equal, which is exactly why we built our own.

MySQL Binary Log Overview

Capturing incremental changes from a MySQL database using the Binary Log is a reliable method. The Binary Log is a built-in feature of MySQL designed to provide a durable and consistent record of all changes made to the database. Here are a few aspects that contribute to the reliability of this approach:

Transactional Consistency: The Binary Log records changes at the transaction level, ensuring that all modifications within a single transaction are captured as a unit. This guarantees consistency and maintains data integrity when capturing and applying changes.

Durability: The Binary Log is typically stored on disk, making it persistent and resistant to data loss. As long as the Binary Log files are retained, you can rely on them to capture changes accurately, even in the event of system restarts or failures.

Replication Protocol: The protocol used to stream Binary Log events from the MySQL server to the CDC application is designed for reliability and fault tolerance. It ensures that events are transmitted reliably over the network and can handle intermittent network issues or temporary interruptions.

Point-in-Time Recovery: The Binary Log allows you to perform point-in-time recovery, which is crucial for data consistency and reliability. In case of database restoration or disaster recovery scenarios, you can use the Binary Log to restore the database to a specific point in time, applying the captured changes to achieve consistency.

While capturing changes from the Binary Log is generally reliable, it’s important to consider a few factors:

Binary Log Configuration: Ensure that binary logging is properly configured on the MySQL server and that the appropriate settings are in place to meet your reliability requirements.

Monitoring and Maintenance: Regularly monitor the Binary Log files to ensure they are being generated and written correctly. Implement proper backup and archiving strategies to avoid data loss or corruption.

Error Handling and Monitoring: Implement robust error-handling mechanisms in your CDC application to handle any issues that may arise during the capture process. Monitor the CDC process for any potential errors or failures and have appropriate alerting mechanisms in place.

Testing and Validation: Thoroughly test and validate your CDC implementation to ensure that changes are accurately captured and applied. Perform tests against various scenarios, including system failures, network interruptions, and high-load situations, to verify the reliability of your solution.

By considering these factors, closely monitoring the process, and following best practices, capturing incremental changes from a MySQL database using the Binary Log can be a reliable and effective method for Change Data Capture.

Postgres WAL Overview

Capturing incremental changes from a Postgres database using the Write Ahead Log (WAL) is considered a highly reliable method. The WAL is a core component of Postgres designed to ensure durability, consistency, and recoverability. Here are some factors that contribute to the reliability of capturing changes using the WAL:

Transactional Consistency: The WAL records changes at the transaction level, guaranteeing that modifications within a transaction are captured atomically. This ensures that captured changes maintain data consistency and integrity.

Durability: The WAL is stored on disk, making it durable and resistant to data loss. It provides a persistent and reliable record of all changes made to the database, even in the event of system failures or crashes.

Write-Ahead Logging: Postgres follows a write-ahead logging approach, where changes are written to the WAL before being applied to the main data files. This guarantees that changes are captured in the log before they are persisted to the database, ensuring that they are available for CDC purposes.

Point-in-Time Recovery: The WAL allows for point-in-time recovery, which is crucial for maintaining data consistency and reliability. By replaying the WAL, you can restore the database to a specific point in time, applying the captured changes to achieve consistency.

Streaming Replication: Postgres provides a built-in streaming replication mechanism that uses the WAL to replicate changes to standby servers. This replication mechanism is highly reliable and can be leveraged for capturing changes for CDC purposes as well.

Crash Recovery: In the event of a crash or unexpected shutdown, Postgres has a crash recovery mechanism that ensures the integrity of the database. The WAL is used during recovery to bring the database back to a consistent state, including applying any unprocessed changes.

While capturing changes from the WAL is generally reliable, it’s important to consider the following factors:

WAL Archiving: Ensure that WAL archiving is properly configured and that you have appropriate strategies for archiving and retaining WAL files. This is crucial for maintaining a complete and reliable history of changes.

Monitoring and Maintenance: Regularly monitor the WAL files to ensure they are being generated and stored correctly. Implement proper backup and retention strategies to avoid data loss or corruption.

Error Handling and Monitoring: Implement robust error-handling mechanisms in your CDC application to handle any issues that may arise during the capture process. Monitor the CDC process for potential errors or failures and have appropriate alerting mechanisms.

Testing and Validation: Thoroughly test and validate your CDC implementation to ensure that changes are accurately captured and applied. Perform tests against various scenarios, including system failures, network interruptions, and high-load situations, to verify the reliability of your solution.

By considering these factors, closely monitoring the process, and following best practices, capturing incremental changes from a Postgres database using the Write Ahead Log can be a highly reliable method for Change Data Capture.

SQL Server CDC Overview

Capturing incremental changes from a SQL Server database using Change Data Capture (CDC) is considered a reliable method. CDC is a native feature of SQL Server specifically designed for capturing and tracking changes made to tables. Here are several factors that contribute to the reliability of CDC:

Transactional Consistency: CDC captures changes at the transaction level, ensuring that modifications within a transaction are captured as a single unit. This guarantees data consistency and integrity when capturing and applying changes.

Built-in Support: CDC is a native feature provided by SQL Server, meaning it is developed and maintained by Microsoft, the creators of SQL Server. As a built-in feature, it is designed to work reliably and seamlessly with the database engine.

Log Scanning: CDC leverages transaction log scanning to identify and capture changes made to tables. The transaction log is a durable and reliable component of SQL Server that records all changes to the database. CDC uses this log to accurately capture the changes without impacting the performance of the database.

Low Overhead: CDC is designed to have minimal impact on the performance of the database. The log scanning process is optimized to efficiently extract and capture changes, ensuring that the CDC operation does not significantly impact the overall system performance.

Replication Support: CDC provides built-in support for replicating the captured changes to other databases. SQL Server’s replication mechanisms are well-established and proven to be reliable, ensuring that changes are reliably propagated to the target databases.

Change Tracking Tables: CDC creates change tracking tables that store the captured changes. These tables are part of the system and are managed by SQL Server, ensuring their reliability and consistency.

Resilience: CDC includes mechanisms to handle failures and ensure the reliability of the change capture process. It can handle interrupted or failed operations and provides methods for managing and recovering from errors.

Point-in-Time Recovery: CDC allows for point-in-time recovery by tracking the changes made to the database. This enables you to restore the database to a specific point in time and apply the captured changes, ensuring data consistency and reliability.

While CDC is generally reliable, it’s essential to consider the following factors:

Proper Configuration: CDC requires appropriate configuration and setup within the SQL Server instance. It is crucial to follow the recommended guidelines and best practices provided by Microsoft to ensure the reliability of CDC.

Monitoring and Maintenance: Regularly monitor the CDC process and ensure that it is functioning correctly. Implement proper backup and maintenance strategies to safeguard the change tracking tables and related metadata.

Error Handling and Monitoring: Implement robust error handling mechanisms in your CDC application to handle any issues that may occur during the capture and replication process. Monitor the CDC process for potential errors or failures and have appropriate alerting mechanisms in place.

By considering these factors, closely monitoring the process, and following Microsoft’s guidelines, capturing incremental changes from a SQL Server database using Change Data Capture can be a reliable method for Change Data Capture.

SQL Server Change Tracking Overview

Capturing incremental changes from a SQL Server database using Change Tracking is considered a reliable method. Change Tracking is a native feature provided by SQL Server to track changes made to user tables. Here are several factors that contribute to the reliability of Change Tracking:

Transactional Consistency: Change Tracking captures changes at the transaction level, ensuring that modifications within a transaction are tracked as a single unit. This guarantees data consistency and integrity when capturing and processing changes.

Built-in Support: Change Tracking is a built-in feature of SQL Server, developed and maintained by Microsoft. As a native feature, it is designed to work reliably and seamlessly with the database engine.

Low Overhead: Change Tracking is designed to have minimal impact on the performance of the database. It operates by scanning the transaction log for changes, without significantly affecting the overall system performance.

Change Tracking Tables: SQL Server creates change tracking tables that store the tracked changes. These tables are managed by SQL Server and ensure the reliability and consistency of the captured data.

Granular Tracking: Change Tracking allows you to track specific columns of interest, providing granular control over the changes you capture. This flexibility ensures that only relevant changes are tracked, reducing unnecessary overhead.

Incremental Tracking: Change Tracking operates in an incremental manner, only capturing and storing the changes that occur after the last synchronization. This approach improves efficiency and reduces the processing overhead for capturing subsequent changes.

Resilience: Change Tracking includes mechanisms to handle failures and ensure the reliability of the change capture process. It can handle interrupted or failed operations and provides methods for managing and recovering from errors.

Point-in-Time Recovery: Change Tracking, combined with other database backup and recovery mechanisms, enables point-in-time recovery. By leveraging the tracked changes, you can restore the database to a specific point in time and apply the captured changes to achieve data consistency and reliability.

While Change Tracking is generally reliable, it’s important to consider the following factors:

Proper Configuration: Change Tracking requires proper configuration and setup within the SQL Server instance. Ensure that Change Tracking is enabled for the relevant tables and follow the recommended guidelines provided by Microsoft to ensure reliability.

Monitoring and Maintenance: Regularly monitor the Change Tracking process to ensure it is functioning correctly. Implement appropriate backup and maintenance strategies to safeguard the change-tracking metadata and tables.

Error Handling and Monitoring: Implement robust error-handling mechanisms in your application to handle any issues that may occur during the capture and processing of changes. Monitor the Change Tracking process for potential errors or failures and have appropriate alerting mechanisms in place.

By considering these factors, closely monitoring the process, and following Microsoft’s guidelines, capturing incremental changes from a SQL Server database using Change Tracking can be a reliable method for Change Data Capture.

MongoDB Change Streams Overview

Capturing incremental changes from a MongoDB database using Change Streams is generally considered a reliable method. Change Streams is a feature introduced in MongoDB version 3.6 that allows you to track changes in real-time at the collection level. Here are several factors that contribute to the reliability of Change Streams:

Real-Time Capture: Change Streams provide real-time change capture, allowing you to receive notifications of changes as they happen in the database. This ensures timely and accurate capture of incremental changes.

Event-Driven Architecture: Change Streams use an event-driven architecture to propagate changes. The MongoDB server pushes change events to subscribing applications, minimizing the delay between the occurrence of a change and its capture.

Resilience: MongoDB provides built-in mechanisms for handling network interruptions and failover scenarios. Change Streams can automatically reconnect and resume tracking changes when connectivity is restored or in the event of server failover, ensuring the reliability of the capture process.

Scalability: Change Streams can be distributed across replica sets or sharded clusters in MongoDB, allowing for high availability and scalability. This enables capturing changes reliably even in large-scale and distributed environments.

Efficient and Low Overhead: Change Streams are designed to have low overhead on the MongoDB server. They utilize the underlying replication infrastructure and oplog (operation log) to efficiently track changes without impacting the performance of the database.

Consistent Ordering: Change Streams guarantee the order of captured changes based on their occurrence in the oplog. This ensures that changes are captured and processed in the same order as they were applied to the database, maintaining data consistency.

Failure Handling: Change Streams provide mechanisms to handle failures and ensure reliability. Applications can acknowledge and process changes, ensuring that changes are not lost if the subscribing application encounters temporary errors or issues.

Change Event Structure: Change Streams provide detailed change events that include information such as the type of change (insert, update, delete), the affected document, and other relevant metadata. This struc^[[Oture enhances the reliability and flexibility of capturing and processing changes.

While Change Streams are generally reliable, it’s important to consider the following factors:

Proper Configuration: Ensure that Change Streams are properly configured on the MongoDB server and that the necessary permissions are granted to subscribing applications. Follow the MongoDB documentation and best practices for optimal configuration.

Monitoring and Error Handling: Monitor the Change Streams process and implement appropriate error handling mechanisms in your application to handle potential issues or failures. Have proper alerting and monitoring in place to detect and address any problems that may occur.

Compatibility and Versioning: Keep in mind that Change Streams were introduced in MongoDB version 3.6. Ensure that you are using a compatible version of MongoDB that supports Change Streams and consider any version-specific considerations or limitations.

By considering these factors, closely monitoring the process, and following MongoDB’s guidelines, capturing incremental changes from a MongoDB database using Change Streams can be a reliable method for Change Data Capture.

Oracle Redo Logs Overview

Capturing incremental changes from an Oracle database can be done using various methods. This includes Oracle XStreams (requires a GoldenGate licensing) as well as Oracle Redo Logs.

Redo Logs: Using Oracle’s Redo Logo for CDC purposes, involves reading and analyzing the redo log files with the help of Oracle’s built-in utility LogMiner. LogMiner helps to capture and convert real-time data changes written to the Redo Logs at the transaction level, providing a low-overhead method for real-time data capture and replication.

Streaming Replication:Originally, the Redo Log files enable the Server or DBA to re-do transactions in case a recovery operation is needed.

While capturing changes from the Redo Logs is generally reliable, it’s important to consider the following factors:

Data Availability:Since reading the changes is happening from the archived Redo Logs, the target database can take up to 5 min to be updated with the recent changes.

Table and Column Names:LogMiner requires table and column names that are 30 characters or less. Therefore, one must ensure that table and column names adhere to this limit to avoid errors when using CDC via LogMiner.

By considering these factors, closely monitoring the process, and following best practices, capturing incremental changes from an Oracle database using the Redo Logs can be a reliable and effective method for Change Data Capture.

Let’s recap

Change Data Capture (CDC) is a powerful and reliable method for incremental replication from various databases, such as MySQL, Postgres, SQL Server, Oracle, and MongoDB. Each database system needs to use a different CDC method to handle incremental replication. Whether using Binary Log in MySQL or Change Streams in MongoDB, an efficient CDC method is required for transactional consistency, durability, point-in-time recovery, and low overhead.

By adhering to best practices, organizations can confidently utilize CDC for data integration and synchronization, ensuring data consistency and integrity across distributed systems. Check out our 6 Musts for Headache-Free Change Data Capture (CDC)– it covers just what you need.

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